Introduction to Data Warehouse Automation
A data warehouse is a way to provide business analysts and other users with a centralized repository of enterprise data from which to glean insights that guide business decisions. What’s changed since data warehousing was first introduced in the 1970s is that traditional, waterfall data warehousing software development processes are no longer viable for planning, designing, developing and operating data warehouses. The traditional approach has proven to be too slow, too manual and too complex to deliver what the business needs when it needs it. That’s where data automation and data warehouse automation solutions can help.
What is data warehouse automation?
Data warehouse automation (DWA) helps IT teams deliver and manage much more than before, much faster, with less project risk and at a lower cost by eliminating repetitive design, development, deployment and operational tasks within the data warehouse lifecycle.
The Data Warehouse Institute (TDWI) defines data warehouse automation as:
“…using technology to gain efficiencies and improve effectiveness in data warehousing processes. Data warehouse automation is much more than simply automating the development process. It encompasses all of the core processes of data warehousing including design, development, testing, deployment, operations, impacts analysis, and change management.”
With automated data warehousing, IT teams can fast-track new data integration, more effectively work with big data, and devote greater time to the business intelligence initiatives that will yield the greatest impact for their organizations.
Data warehouse automation software combines the use of metadata, data warehousing methodologies, pattern detection and more to help developers autogenerate data warehouse designs and coding using data warehouse design tools and timesaving development wizards and templates. By using data warehouse automation to address much of the time-intensive, repetitive work previously required in the hand-coding of data warehousing software projects, developers can instead focus on more strategic elements of data warehousing, and ensuring the data warehouse delivered will meet the evolving needs of the business.
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